Taxpayers, such as, for example, individuals and companies, may file income tax returns yearly. Consequently, based on a taxpayer's gross income and possible deductions and exemptions available, a taxpayer may either owe taxes or receive a refund for taxes paid during the year. Accordingly, it is common for a taxpayer to attempt to reduce his taxable income, and associated income tax liability, to either pay as little tax as possible or receive as large a tax refund as possible.
In order to reduce the amount of income tax liability, a taxpayer may, for example, deduct expenses that are not legally available, claim more dependents than allowed by law, fail to report all earned income, or perform other such illegal activities. These are examples of fraudulent actions that a taxpayer may take to reduce income tax liability. In addition, a taxpayer may purchase or steal one or more false identities and file fraudulent tax returns in an effort to receive a tax refund. These are other examples of fraudulent activity.
Such examples are prevalent and are increasingly common and difficult to catch by the tax receiving agency. For example, over 130 million tax returns were filed with the Internal Revenue Service (“IRS”) in 2007. A corresponding number of tax returns were also filed in the corresponding state(s) of residence for each taxpayer. Therefore, the large volume of filed tax returns results in a large number of fraudulent tax returns.
One way to identify a fraudulent tax return would be to audit the return, e.g., by comparing it to previously filed returns, historical data, and other relevant information. When applying analytics to determine a fraud risk of a particular transaction (e.g. tax return processing), it is desirable to apply one or more risk rules to data contained in the transaction and/or to a broad range of stored historical data. Current systems typically conduct a manual analysis, normally after the peak return filing season is complete. However, the manual nature of the analysis may limit both the scope of the analysis that can be completed and the timeliness of the analysis. Current systems may also conduct an automated analysis, but this analysis is often undertaken on an individual transaction basis and is not used to monitor for new risk that can be discovered through a system-wide analysis.
In current systems, data analysis is a common method for discovering patterns of fraud and non-compliance. Specifically, trends and patterns in the data may be modeled, and changes and anomalies in the trends may be reviewed to determine if they are related to fraud. However, the analysis conducted in current systems may not detect new issues until a significant time has passed after fraudulent acts are committed. Therefore, during the time prior to detection, fraud can continue to occur, and the perpetrators of the fraud may avoid detection.
In current systems, there is a need to allow revenue (e.g. tax) agencies to monitor for changes in systemic risk. Accordingly, there is a need to create statistical summaries of one or more fields of data contained in one or more forms submitted to the revenue agencies, statistically determine patterns in the statistical summaries, and monitor for changes in the patterns. Furthermore, when a change or anomaly is detected, there is a need to provide an alert to conduct an investigation of the change or anomaly to determine if it is a result of fraud or abuse.
Accordingly, there is a need to automate the monitoring of risk analysis so that it may be executed repeatedly over the course of a filing season, thereby achieving a wider range of anomaly and pattern detection to be applied than could be applied manually. In particular, a system is needed that provides data structures which may summarize base data patterns on a predetermined (e.g. daily, weekly, monthly, yearly) basis to provide for an analysis and comparison of the patterns.
Therefore, a system is needed that enables new types of fraud to be detected more quickly, thereby sending an alert to review the data to determine possible fraud to block fraudulent filings. To address these needs, a system is needed that may automatically monitor patterns of risk of one or more tax returns.